DOS2 (version 0.5.2)

periodontal: Smoking and Periodontal Disease

Description

Data from NHANES 2011-2012 containing 441 matched pairs of a daily cigarette smoker and a never smoker, recording the extent of periodontal disease. See Rosenbaum (2017) and Chapter 20 of "Design of Observational Studies", second edition.

Usage

data("periodontal")

Format

A data frame with 882 observations on the following 12 variables.

SEQN

NHANES 2011-2012 sequence number

female

=1 for female, 0 for male

age

Age in years

black

=1 for black, 0 for other

educf

Education, in five categories. An ordered factor with levels <9 for less than 9th grade, 9 to 11 for 9th to 11th grade, HS/GED for high school or GED degree, SomeCol for some college, College for college degree.

income

Ratio of family income to the poverty level, capped at 5 times the poverty level.

cigsperday

Cigarettes smoked per day for daily smokers, 0 for never smokers

either

Number of periodonal measurements indicative of periodontal disease.

neither

Number of periodonal measurements

pcteither

Percent indicative of periodontal disease, =100*either/neither.

z

Treatment indicator, 1=daily smoker, 0=never smoker

mset

Matched set indicator, 1 to 441.

Details

Excluding wisdom teeth, 6 measurements are taken for each tooth that is present, up to 28 teeth. Following Tomar and Asma (2000), a measurement indicates periodontal disease if either there is a loss of attachment of at least 4mm or a pocket depth of at least 4mm. The first individual has 11 measurements indicative of periodontal disease, out of 106 measurements, so pcteither is 100*11/106 = 10.38 percent. A related data set in DOS2 with bivariate outcome is teeth.

References

Rosenbaum, P. R. (2015) <https://obsstudies.org/two-r-packages-for-sensitivity-analysis-in-observational-studies/> "Two R packages for sensitivity analysis in observational studies". Observational Studies, 1(1), 1-17.

Rosenbaum, P. R. (2017) <doi:10.1214/17-STS621> "The general structure of evidence factors in observational studies". Statistical Science 32, 514-530.

Tomar, S. L. and Asma, S. (2000) <doi:10.1902/jop.2000.71.5.743> "Smoking attributable periodontitis in the US: Findings from NHANES III". J Periodont 71, 743-751.

"US National Health and Nutrition Examination Survey 2011-2012". www.cdc.gov/nchs/nhanes/index.htm

Examples

# NOT RUN {
# Figure 1 in Rosenbaum (2017)
data(periodontal)
attach(periodontal)
oldpar<-par()
m<-matrix(1:2,1,2)
layout(m,widths=c(1,2))
boxplot(pcteither[z==1]-pcteither[z==0],ylab="Smoker-Control Difference",
        main="(i)",xlab="Matched Pairs",ylim=c(-100,100))
abline(h=0,lty=2)
crosscutplot(cigsperday[z==1],pcteither[z==1]-pcteither[z==0],ylab="Smoker-Control Difference",
    xlab="Cigarettes per Day",main="(ii)",ylim=c(-100,100))
abline(h=0,lty=2)

# Sensitivity analysis in Section 2.3 of Rosenbaum (2017)
y<-pcteither[z==1]-pcteither[z==0]
x<-cigsperday[z==1]
senWilcox(y,gamma=2.76)
# The following is the same as sensitivitymw::senmw(y,gamma=2.77,method="p")
sensitivitymult::senm(pcteither,z,mset,gamma=2.77,inner=.5,trim=2)
# The following is the same as sensitivitymw::senmw(y,gamma=3.5,method="p")
sensitivitymult::senm(pcteither,z,mset,gamma=3.5,inner=.5,trim=2)
# Second evidence factor
crosscut(x,y)
crosscut(x,y,gamma=1.6)

# Note, however, that other statistics report greater insensitivity to
# bias by virtue of having larger design sensitivity:
sensitivitymult::senm(pcteither,z,mset,gamma=3.5,inner=1,trim=4)
sensitivitymult::senm(pcteither,z,mset,gamma=4.2,inner=1,trim=4)
senU(y,m1=4,m2=5,m=5,gamma=2.77)
senU(y,m1=6,m2=8,m=8,gamma=2.77)
senU(y,m1=6,m2=8,m=8,gamma=3.5)
detach(periodontal)
par(oldpar)
# }